OpenRaiser/Intern-Atlas huggingface dataset:
The Influence of Variable Frame Timing on First-Person Gaming
Venue: arXiv cs.GR 2023
Year: 2023

Variable frame timing (VFT), or changes in the time intervals between discrete frame images displayed to users, deviates from our traditional conceptualization of frame rate in which all frame times are equal. With the advent of variable refresh rate (VRR) monitor technologies, gamers experience VFT at the display. VRR, coupled with increased display refresh rates and high-end hardware, enables smoother variation of frame presentation sequences. We assess the effects of VFT on the perception of smoothness (experiment 1) and performance (experiment 2) in first-person shooter (FPS) gameplay by introducing frequent but relatively small (4-12 ms) variations in frame time around typical refresh rates (30-240 Hz). Our results indicate that VFT impacts the perception of smoothness. However, the results from experiment 2 do not indicate differences in FPS task performance (i.e., completion time) between variable and constant frame time sequences ranked equally smooth in experiment 1.
Evaluating Language Models for Mathematics through Interactions
Venue: arXiv cs.LG 2023
Year: 2023

There is much excitement about the opportunity to harness the power of large language models (LLMs) when building problem-solving assistants. However, the standard methodology of evaluating LLMs relies on static pairs of inputs and outputs, and is insufficient for making an informed decision about which LLMs and under which assistive settings can they be sensibly used. Static assessment fails to account for the essential interactive element in LLM deployment, and therefore limits how we understand language model capabilities. We introduce CheckMate, an adaptable prototype platform for humans to interact with and evaluate LLMs. We conduct a study with CheckMate to evaluate three language models (InstructGPT, ChatGPT, and GPT-4) as assistants in proving undergraduate-level mathematics, with a mixed cohort of participants from undergraduate students to professors of mathematics. We release the resulting interaction and rating dataset, MathConverse. By analysing MathConverse, we derive a taxonomy of human behaviours and uncover that despite a generally positive correlation, there are notable instances of divergence between correctness and perceived helpfulness in LLM generations, amongst other findings. Further, we garner a more granular understanding of GPT-4 mathematical problem-solving through a series of case studies, contributed by expert mathematicians. We conclude with actionable takeaways for ML practitioners and mathematicians: models that communicate uncertainty respond well to user corrections, and are more interpretable and concise may constitute better assistants. Interactive evaluation is a promising way to navigate the capability of these models; humans should be aware of language models' algebraic fallibility and discern where they are appropriate to use.
Is Generative Modeling-based Stylization Necessary for Domain Adaptation in Regression Tasks?
Venue: arXiv cs.CV 2023
Year: 2023

Unsupervised domain adaptation (UDA) aims to bridge the gap between source and target domains in the absence of target domain labels using two main techniques: input-level alignment (such as generative modeling and stylization) and feature-level alignment (which matches the distribution of the feature maps, e.g. gradient reversal layers). Motivated from the success of generative modeling for image classification, stylization-based methods were recently proposed for regression tasks, such as pose estimation. However, use of input-level alignment via generative modeling and stylization incur additional overhead and computational complexity which limit their use in real-world DA tasks. To investigate the role of input-level alignment for DA, we ask the following question: Is generative modeling-based stylization necessary for visual domain adaptation in regression? Surprisingly, we find that input-alignment has little effect on regression tasks as compared to classification. Based on these insights, we develop a non-parametric feature-level domain alignment method -- Implicit Stylization (ImSty) -- which results in consistent improvements over SOTA regression task, without the need for computationally intensive stylization and generative modeling. Our work conducts a critical evaluation of the role of generative modeling and stylization, at a time when these are also gaining popularity for domain generalization.
Graph Sparsification for GCN Towards Optimal Crop Yield Predictions
Venue: arXiv cs.LG 2023
Year: 2023

In agronomics, predicting crop yield at a per field/county granularity is important for farmers to minimize uncertainty and plan seeding for the next crop cycle. While state-of-the-art prediction techniques employ graph convolutional nets (GCN) to predict future crop yields given relevant features and crop yields of previous years, a dense underlying graph kernel requires long training and execution time. In this paper, we propose a graph sparsification method based on the Fiedler number to remove edges from a complete graph kernel, in order to lower the complexity of GCN training/execution. Specifically, we first show that greedily removing an edge at a time that induces the minimal change in the second eigenvalue leads to a sparse graph with good GCN performance. We then propose a fast method to choose an edge for removal per iteration based on an eigenvalue perturbation theorem. Experiments show that our Fiedler-based method produces a sparse graph with good GCN performance compared to other graph sparsification schemes in crop yield prediction.
Improving Generalization in Task-oriented Dialogues with Workflows and Action Plans
Venue: arXiv cs.CL 2023
Year: 2023

Task-oriented dialogue is difficult in part because it involves understanding user intent, collecting information from the user, executing API calls, and generating helpful and fluent responses. However, for complex tasks one must also correctly do all of these things over multiple steps, and in a specific order. While large pre-trained language models can be fine-tuned end-to-end to create multi-step task-oriented dialogue agents that generate fluent text, our experiments confirm that this approach alone cannot reliably perform new multi-step tasks that are unseen during training. To address these limitations, we augment the dialogue contexts given to \textmd{text2text} transformers with known \textit{valid workflow names} and \textit{action plans}. Action plans consist of sequences of actions required to accomplish a task, and are encoded as simple sequences of keywords (e.g. verify-identity, pull-up-account, reset-password, etc.). We perform extensive experiments on the Action-Based Conversations Dataset (ABCD) with T5-small, base and large models, and show that such models: a) are able to more readily generalize to unseen workflows by following the provided plan, and b) are able to generalize to executing unseen actions if they are provided in the plan. In contrast, models are unable to fully accomplish new multi-step tasks when they are not provided action plan information, even when given new valid workflow names.
Video Colorization with Pre-trained Text-to-Image Diffusion Models
Venue: arXiv cs.CV 2023
Year: 2023

Video colorization is a challenging task that involves inferring plausible and temporally consistent colors for grayscale frames. In this paper, we present ColorDiffuser, an adaptation of a pre-trained text-to-image latent diffusion model for video colorization. With the proposed adapter-based approach, we repropose the pre-trained text-to-image model to accept input grayscale video frames, with the optional text description, for video colorization. To enhance the temporal coherence and maintain the vividness of colorization across frames, we propose two novel techniques: the Color Propagation Attention and Alternated Sampling Strategy. Color Propagation Attention enables the model to refine its colorization decision based on a reference latent frame, while Alternated Sampling Strategy captures spatiotemporal dependencies by using the next and previous adjacent latent frames alternatively as reference during the generative diffusion sampling steps. This encourages bidirectional color information propagation between adjacent video frames, leading to improved color consistency across frames. We conduct extensive experiments on benchmark datasets, and the results demonstrate the effectiveness of our proposed framework. The evaluations show that ColorDiffuser achieves state-of-the-art performance in video colorization, surpassing existing methods in terms of color fidelity, temporal consistency, and visual quality.
An Application of Neutrosophic Sets to Decision Making
Venue: arXiv cs.AI 2023
Year: 2023

Maji et al. introduced in 2002 a method of parametric decision making using soft sets as tools and representing their tabular form as a binary matrix. In cases, however, where some or all of the parameters used for the characterization of the elements of the universal set are of fuzzy texture, their method does not give always the best decision making solution. In order to tackle this problem, we modified in earlier works the method of Maji et al. by replacing the binary elements in the tabular form of the corresponding soft set either by grey numbers or by triangular fuzzy numbers. In this work, in order to tackle more efficiently cases in which the decision maker has doubts about the correctness of the fuzzy/qualitative characterizations assigned to some or all of the elements of the universal set, we replace the binary elements of the tabular form by neutrosophic triplets. Our new, neutrosophic decision making method is illustrated by an application concerning the choice of a new player by a soccer club.
Bio-inspired Dual-auger Self-burrowing Robots in Granular Media
Venue: arXiv cs.RO 2023
Year: 2023

It has been found that certain biological organisms, such as Erodium seeds and Scincus scincus, are capable of effectively and efficiently burying themselves in soil. Biological Organisms employ various locomotion modes, including coiling and uncoiling motions, asymmetric body twisting, and undulating movements that generate motion waves. The coiling-uncoiling motion drives a seed awn to bury itself like a corkscrew, while sandfish skinks use undulatory swimming, which can be thought of as a 2D version of helical motion. Studying burrowing behavior aims to understand how animals navigate underground, whether in their natural burrows or underground habitats, and to implement this knowledge in solving geotechnical penetration problems. Underground horizontal burrowing is challenging due to overcoming the resistance of interaction forces of granular media to move forward. Inspired by the burrowing behavior of seed-awn and sandfish skink, a horizontal self-burrowing robot is developed. The robot is driven by two augers and stabilized by a fin structure. The robot's burrowing behavior is studied in a laboratory setting. It is found that rotation and propulsive motion along the axis of the auger's helical shape significantly reduce granular media's resistance against horizontal penetration by breaking kinematic symmetry or granular media boundary. Additional thrusting and dragging tests were performed to examine the propulsive and resistive forces and unify the observed burrowing behaviors. The tests revealed that the rotation of an auger not only reduces the resistive force and generates a propulsive force, which is influenced by the auger geometry, rotational speed, and direction. As a result, the burrowing behavior of the robot can be predicted using the geometry-rotation-force relations.
CSI-Based Efficient Self-Quarantine Monitoring System Using Branchy Convolution Neural Network
Venue: arXiv cs.CV 2023
Year: 2023

Nowadays, Coronavirus disease (COVID-19) has become a global pandemic because of its fast spread in various countries. To build an anti-epidemic barrier, self-isolation is required for people who have been to any at-risk places or have been in close contact with infected people. However, existing camera or wearable device-based monitoring systems may present privacy leakage risks or cause user inconvenience in some cases. In this paper, we propose a Wi-Fi-based device-free self-quarantine monitoring system. Specifically, we exploit channel state information (CSI) derived from Wi-Fi signals as human activity features. We collect CSI data in a simulated self-quarantine scenario and present BranchyGhostNet, a lightweight convolution neural network (CNN) with an early exit prediction branch, for the efficient joint task of room occupancy detection (ROD) and human activity recognition (HAR). The early exiting branch is used for ROD, and the final one is used for HAR. Our experimental results indicate that the proposed model can achieve an average accuracy of 98.19% for classifying five different human activities. They also confirm that after leveraging the early exit prediction mechanism, the inference latency for ROD can be significantly reduced by 54.04% when compared with the final exiting branch while guaranteeing the accuracy of ROD.
Distinguishing Human Generated Text From ChatGPT Generated Text Using Machine Learning
Venue: arXiv cs.CL 2023
Year: 2023

ChatGPT is a conversational artificial intelligence that is a member of the generative pre-trained transformer of the large language model family. This text generative model was fine-tuned by both supervised learning and reinforcement learning so that it can produce text documents that seem to be written by natural intelligence. Although there are numerous advantages of this generative model, it comes with some reasonable concerns as well. This paper presents a machine learning-based solution that can identify the ChatGPT delivered text from the human written text along with the comparative analysis of a total of 11 machine learning and deep learning algorithms in the classification process. We have tested the proposed model on a Kaggle dataset consisting of 10,000 texts out of which 5,204 texts were written by humans and collected from news and social media. On the corpus generated by GPT-3.5, the proposed algorithm presents an accuracy of 77%.
Voluminous yet Vacuous? Semantic Capital in an Age of Large Language Models
Venue: arXiv cs.CY 2023
Year: 2023

Large Language Models (LLMs) have emerged as transformative forces in the realm of natural language processing, wielding the power to generate human-like text. However, despite their potential for content creation, they carry the risk of eroding our Semantic Capital (SC) - the collective knowledge within our digital ecosystem - thereby posing diverse social epistemic challenges. This paper explores the evolution, capabilities, and limitations of these models, while highlighting ethical concerns they raise. The study contribution is two-fold: first, it is acknowledged that, withstanding the challenges of tracking and controlling LLM impacts, it is necessary to reconsider our interaction with these AI technologies and the narratives that form public perception of them. It is argued that before achieving this goal, it is essential to confront a potential deontological tipping point in an increasing AI-driven infosphere. This goes beyond just adhering to AI ethical norms or regulations and requires understanding the spectrum of social epistemic risks LLMs might bring to our collective SC. Secondly, building on Luciano Floridi's taxonomy for SC risks, those are mapped within the functionality and constraints of LLMs. By this outlook, we aim to protect and enrich our SC while fostering a collaborative environment between humans and AI that augments human intelligence rather than replacing it.
Simulation of a first prototypical 3D solution for Indoor Localization based on Directed and Reflected Signals
Venue: arXiv cs.RO 2023
Year: 2023

We introduce a solution for a specific case of Indoor Localization which involves a directed signal, a reflected signal from the wall and the time difference between them. This solution includes robust localization with a given wall, finding the right wall from a group of walls, obtaining the reflecting wall from measurements, using averaging techniques for improving measurements with errors and successfully grouping measurements regarding reflecting walls. It also includes performing self-calibration by computation of wall distance and direction introducing algorithms such as All pairs, Disjoint pairs and Overlapping pairs and clustering walls based on Inversion and Gnomonic Projection. Several of these algorithms are then compared in order to ameliorate the effects of measurement errors.
The Effect of News Article Quality on Ad Consumption
Venue: arXiv cs.IR 2023
Year: 2023

Practical news feed platforms generate a hybrid list of news articles and advertising items (e.g., products, services, or information) and many platforms optimize the position of news articles and advertisements independently. However, they should be arranged with careful consideration of each other, as we show in this study, since user behaviors toward advertisements are significantly affected by the news articles. This paper investigates the effect of news articles on users' ad consumption and shows the dependency between news and ad effectiveness. We conducted a service log analysis and showed that sessions with high-quality news article exposure had more ad consumption than those with low-quality news article exposure. Based on this result, we hypothesized that exposure to high-quality articles will lead to a high ad consumption rate. Thus, we conducted million-scale A/B testing to investigate the effect of high-quality articles on ad consumption, in which we prioritized high-quality articles in the ranking for the treatment group. The A/B test showed that the treatment group's ad consumption, such as the number of clicks, conversions, and sales, increased significantly while the number of article clicks decreased. We also found that users who prefer a social or economic topic had more ad consumption by stratified analysis. These insights regarding news articles and advertisements will help optimize news and ad effectiveness in rankings considering their mutual influence.
Software Development Vehicles to enable extended and early co-design: a RISC-V and HPC case of study
Venue: arXiv cs.OH 2023
Year: 2023

Prototyping HPC systems with low-to-mid technology readiness level (TRL) systems is critical for providing feedback to hardware designers, the system software team (e.g., compiler developers), and early adopters from the scientific community. The typical approach to hardware design and HPC system prototyping often limits feedback or only allows it at a late stage. In this paper, we present a set of tools for co-designing HPC systems, called software development vehicles (SDV). We use an innovative RISC-V design as a demonstrator, which includes a scalar CPU and a vector processing unit capable of operating large vectors up to 16 kbits. We provide an incremental methodology and early tangible evidence of the co-design process that provide feedback to improve both architecture and system software at a very early stage of system development.
Fast Interactive Search with a Scale-Free Comparison Oracle
Venue: arXiv cs.IR 2023
Year: 2023

A comparison-based search algorithm lets a user find a target item $t$ in a database by answering queries of the form, ``Which of items $i$ and $j$ is closer to $t$?'' Instead of formulating an explicit query (such as one or several keywords), the user navigates towards the target via a sequence of such (typically noisy) queries. We propose a scale-free probabilistic oracle model called $γ$-CKL for such similarity triplets $(i,j;t)$, which generalizes the CKL triplet model proposed in the literature. The generalization affords independent control over the discriminating power of the oracle and the dimension of the feature space containing the items. We develop a search algorithm with provably exponential rate of convergence under the $γ$-CKL oracle, thanks to a backtracking strategy that deals with the unavoidable errors in updating the belief region around the target. We evaluate the performance of the algorithm both over the posited oracle and over several real-world triplet datasets. We also report on a comprehensive user study, where human subjects navigate a database of face portraits.
Beta Thalassemia Carriers detection empowered federated Learning
Venue: arXiv cs.LG 2023
Year: 2023

Thalassemia is a group of inherited blood disorders that happen when hemoglobin, the protein in red blood cells that carries oxygen, is not made enough. It is found all over the body and is needed for survival. If both parents have thalassemia, a child's chance of getting it increases. Genetic counselling and early diagnosis are essential for treating thalassemia and stopping it from being passed on to future generations. It may be hard for healthcare professionals to differentiate between people with thalassemia carriers and those without. The current blood tests for beta thalassemia carriers are too expensive, take too long, and require too much screening equipment. The World Health Organization says there is a high death rate for people with thalassemia. Therefore, it is essential to find thalassemia carriers to act quickly. High-performance liquid chromatography (HPLC), the standard test method, has problems such as cost, time, and equipment needs. So, there must be a quick and cheap way to find people carrying the thalassemia gene. Using federated learning (FL) techniques, this study shows a new way to find people with the beta-thalassemia gene. FL allows data to be collected and processed on-site while following privacy rules, making it an excellent choice for sensitive health data. Researchers used FL to train a model for beta-thalassemia carriers by looking at the complete blood count results and red blood cell indices. The model was 92.38 % accurate at telling the difference between beta-thalassemia carriers and people who did not have the disease. The proposed FL model is better than other published methods in terms of how well it works, how reliable it is, and how private it is. This research shows a promising, quick, accurate, and low-cost way to find thalassemia carriers and opens the door for screening them on a large scale.
ErfReLU: Adaptive Activation Function for Deep Neural Network
Venue: arXiv cs.NE 2023
Year: 2023

Recent research has found that the activation function (AF) selected for adding non-linearity into the output can have a big impact on how effectively deep learning networks perform. Developing activation functions that can adapt simultaneously with learning is a need of time. Researchers recently started developing activation functions that can be trained throughout the learning process, known as trainable, or adaptive activation functions (AAF). Research on AAF that enhance the outcomes is still in its early stages. In this paper, a novel activation function 'ErfReLU' has been developed based on the erf function and ReLU. This function exploits the ReLU and the error function (erf) to its advantage. State of art activation functions like Sigmoid, ReLU, Tanh, and their properties have been briefly explained. Adaptive activation functions like Tanhsoft1, Tanhsoft2, Tanhsoft3, TanhLU, SAAF, ErfAct, Pserf, Smish, and Serf have also been described. Lastly, performance analysis of 9 trainable activation functions along with the proposed one namely Tanhsoft1, Tanhsoft2, Tanhsoft3, TanhLU, SAAF, ErfAct, Pserf, Smish, and Serf has been shown by applying these activation functions in MobileNet, VGG16, and ResNet models on CIFAR-10, MNIST, and FMNIST benchmark datasets.